Neuroscience methods

Evaluating methods

What are we measuring?

  • Structure
  • Activity
    • Why not function?

What is the question?

  • Structure X -> Structure Y
  • Structure X -> Function Y

Evaluating methods

Strengths & Weaknesses

  • Cost
  • Invasiveness
  • Spatial/temporal resolution

…and temporal resolution

(Sejnowski, Churchland, & Movshon, 2014)

Types of methods

Structural

  • Anatomy
  • Connectivity/connectome

Functional (next time)

  • What does it do?
  • Physiology/Activity

Mapping structures

  • Cell/axon stains
  • Cellular distribution, concentration, microanatomy

Golgi stain – whole cells, but small %

http://connectomethebook.com/wp-content/uploads/2011/11/Brainforest17_1119.jpg

Here’s a pretty one of the hippocampus.

http://wam.umn.edu/calendar/cajal/

And here is one from Santiago Ramon y Cajal.

Nissl stain: Only cell bodies

Here’s a Nissl-stained section of the macaque brain. It stains only cell bodies, but the density of staining tells us where there are lots of cells and where there are fewer.

Evaluating cellular techniques

  • Invasive (in humans post-mortem only)
  • High spatial resolution, but poor/coarse temporal
    • Why?

Mapping structures

  • Computed axial tomography (CAT), CT
  • X-ray based

http://img.tfd.com/mk/T/X2604-T-22.png

Magnetic Resonance Imaging (MRI)

  • Magnetic resonance a property of some isotopes and complex molecules
  • Hydrogen (\(H\)), common in water & fat, is one
  • In magnetic field, \(H\) atoms absorb and release radio frequency (RF) energy
  • \(H\) atoms align with strong magnetic field
  • Applying RF pulse perturbs alignment
  • Rate/timing of realignment varies by tissue
  • Realignment gives off radio frequency (RF) signals
  • Strength of RF ~ density of \(H\) (or other target)
  • K-space (frequency/phase) -> anatomical space

Structural MRI

  • Tissue density/type differences
  • Gray matter (nerve cells & dendrites) vs. white matter (axon fibers)
  • Spectroscopy (specific metabolites)
  • Region sizes/volumes

Here is an illustration of the different slices of an image sequence.

Here’s an example of MR spectroscopy showing the concentrations of several different metabolites in a large voxel of brain tissue.

Voxel-based morphometry (VBM)

Volume differences in schizophrenic patients vs. controls

(Pomarol-Clotet et al., 2010)

And here’s an illustration of the use of morphometric techniques. The colored portions are statistical maps placed on top of a base structural map. The statistical maps provide information about the comparison in brain volumes between patients and controls in those areas.

What is the wiring diagram (“connectome”)?

The idea is analogous to electronics. We want the schematic. Without the schematic, we can’t really tell what the thing does.

Retrograde (output -> input) vs. anterograde (input -> output) tracers

http://openi.nlm.nih.gov/imgs/512/348/3176268/3176268_1471-2105-12-351-2.png

Diffusion Tensor Imaging (DTI)

  • Structural MRI technique
  • Diffusion tensor: measurement of spatial pattern of \(H_2O\) diffusion in small volume
  • Uniform (“isotropic”) vs. non-uniform (“anisotropic”)
  • Strong anisotropy suggests large # of axons with similar orientations (fiber tracts)

Here’s an illustration of what a tensor looks like. You can see an isotropic and an anisotropic tensor.

Connectome as matrix

Main points

  • Understanding brain/behavior relations requires a diverse toolkit
    • Structural vs. functional methods
    • Spatial and temporal resolution
    • Invasive vs. non-

References

Lichtman, J. W., Livet, J., & Sanes, J. R. (2008). A technicolour approach to the connectome. Nature Reviews Neuroscience, 9(6), 417–422. https://doi.org/10.1038/nrn2391

Pomarol-Clotet, E., Canales-Rodrı́guez, E. J., Salvador, R., Sarró, S., Gomar, J. J., Vila, F., … McKenna, P. J. (2010). Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging. Mol. Psychiatry, 15(8), 823–830. https://doi.org/10.1038/mp.2009.146

Sejnowski, T. J., Churchland, P. S., & Movshon, J. A. (2014). Putting big data to good use in neuroscience. Nature Neuroscience, 17(11), 1440–1441. https://doi.org/10.1038/nn.3839